verde.minmax

verde.minmax(*args, nan=True, min_percentile=0, max_percentile=100)

Calculate the minimum and maximum values of the given array(s).

Use this to set the limits of your colorbars for non-diverging data.

Parameters:

args

One or more arrays. If more than one are given, a minimum and maximum will be calculated across all arrays.

nanbool, optional

If True, will use the nan version of numpy functions to ignore NaNs.

min_percentilefloat

Return the supplied percentile (0 to 100) instead of the minimum value of the arrays. Defaults to 0, giving the minimum value.

max_percentilefloat

Return the supplied percentile (0 to 100) instead of the maximum value of the arrays. Defaults to 100, giving the maximum value.

Returns:

min, maxfloat

The minimum and maximum (or percentile) values across all arrays.

Examples

>>> result = minmax((1, -10, 25, 2, 3))
>>> tuple(map(float, result))
(-10.0, 25.0)
>>> result = minmax(
...     (1, -10.5, 25, 2), (0.1, 100, -500), (-200, -300, -0.1, -499)
... )
>>> tuple(map(float, result))
(-500.0, 100.0)

If the array contains NaNs, we’ll use the nan version of of the numpy functions by default. You can turn this off through the nan argument.

>>> import numpy as np
>>> result = minmax((1, -10, 25, 2, 3, np.nan))
>>> tuple(map(float, result))
(-10.0, 25.0)
>>> result = minmax((1, -10, 25, 2, 3, np.nan), nan=False)
>>> tuple(map(float, result))
(nan, nan)

If a more robust statistic is desired, you can use min_percentile and or max_percentile to get the values at given percentiles instead of the minimum and maximum.

>>> import numpy as np
>>> result = minmax(
...     (1, -10, 25, 2, 3), min_percentile=2, max_percentile=98
... )
>>> tuple(map(float, result))
(-9.12, 23.24)
>>> result = minmax(
...     (1, -10, 25, 2, 3), min_percentile=0, max_percentile=100
... )
>>> tuple(map(float, result))
(-10.0, 25.0)